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Online 3D point cloud classification and scene understanding are crucial tasks for Unmanned Ground Vehicles (UGVs) equipped with multiple laser scanners. Due to the poor performance of traditional 2D image representation model for 3D point clouds, a novel Optimal Bearing Angle (OBA) model is therefore proposed to overcome the limitations of texture information losing and image blurring caused by the...
This paper investigates real-time road detection for an unmanned ground vehicle (UGV) to navigate in a campus environment. A novel vision system with two monocular cameras at different heights and angles is utilized to accomplish both road region detection and direction estimation tasks simultaneously. An Adaboost-based classifier is used in the road region detection in order to handle the road surface's...
An obstacle detection approach based on stereo vision is proposed for mobile robot navigation. The aim of the approach is to detect a real environment to allow a mobile robot to find a safe path even in complex scenarios. Our novelty is represented in terms of the two-stage perception structure. The detection stage infers the relationship between obstacles and a ground, and determines the region of...
Self-localization methods for intelligent mobile robot can be found from literatures. Here, we studied two information fusion methods, namely extended Kalman filter and unscented Kalman filter. They are used to locate the pose of mobile robot that is navigating in the indoor environment. To analyze the performance of the two filters, they were used respectively to fuse the information coming from...
Localization is an essential task for an autonomous mobile robot's indoor navigation. Based on the metric-topological environment modeling, a hybrid localization method, pose tracking in local metric map and global localization in topological map, are used in this paper to carry out accurate and robust localization in semi-structured indoor environment. In order to guarantee the effectiveness of global...
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